If you are using the Tool Manager to extract data from a database into the MineSet tools, you should understand database structures. It also would be helpful to know SQL.

If you are configuring the tools directly (through the configuration files, or through the command line in the case of the association rules), you should have some knowledge of UNIX as well as some programming experience.

Once the data has been loaded into the various visualization tools, you will not need a database or programming background, although you will be able to interpret the displays more easily if you have an understanding of the data and what it represents.

In addition to this preface, the documentation for MineSet consists of the following chapters:

Chapter 1, “Getting Started”
This provides a brief overview of each MineSet tool and describes the processes that occur when invoking and using a tool.

Chapter 2, “Setting Up MineSet”
This chapter describes how to set up MineSet by configuring the DataMover.

Chapter 3, “The Tool Manager”
This chapter describes the menus and functions of the initial interface for invoking tools and tells how to produce their respective configuration files.

Chapter 4, “Using the Statistics Visualizer”
This chapter provides a description of the Statistics Visualizer. This tool is valuable for comprehending variations in statistics by comparing box plots and histograms.

Chapter 5, “Using the Tree Visualizer”
This chapter provides a complete description of the Tree Visualizer tool interface. This tool is valuable for visualizing hierarchical data.

Chapter 6, “Using the Map Visualizer”
This chapter provides a complete description of the Map Visualizer interface. This tool is valuable for visualizing data that is connected with a geographical location.

Chapter 7, “Using the Scatter Visualizer”
This chapter provides a complete description of the Scatter Visualizer interface. This tool is valuable for visualizing multidimensional data.

Chapter 8, “Using the Splat Visualizer”
This chapter provides a complete description of the Splat Visualizer. This tool, which is particularly well suited for application to very large datasets, lets you visually analyze relationships among several variables, either statically or by animation.

Chapter 9, “Using the Rules Visualizer”
This chapter provides a complete description of the Rules Visualizer. This tool is valuable for mining large datasets and visualizing correlations in that data.

Chapter 10, “MineSet Inducers and Classifiers”
This chapter provides a brief introduction to classifiers and regressors, and the algorithms that generate them, called inducers. Specifically, it introduces the three MineSet classifiers: Decision Tree, Option Tree and Evidence.

Chapter 11, “Inducing and Visualizing the Decision Tree Classifier”
This chapter describes how to generate and use the Decision Tree Classifier. This tool is valuable for classifying data according to a set of attributes by making a series of decisions based on those attributes.

Chapter 12, “Inducing and Visualizing the Option Tree Classifier”
This chapter describes how to generate and use the Option Tree Classifier. This tool assigns each record to a class. Option trees can contain special option nodes that allow the classifier to consider the influence of splitting on multiple attributes simultaneously.

Chapter 13, “Inducing and Visualizing the Evidence Classifier”
This chapter describes how to generate and use the Evidence Classifier. This tool is valuable for classifying data by examining the probabilities of a specified result occurring based on a given attribute.

Chapter 14, “Inducing and Visualizing the Decision Table”
This chapter describes how to generate and use the Decision Table Classifier. This tool is useful for examining data and visualizing correlations between pairs of attributes.

Chapter 15, “Inducing and Visualizing the Regression Tree”
This chapter describes how to generate and use the Regression Tree Classifier. This tool is useful for predicting attributes based on continuous values, such as occur in real life.

Chapter 16, “Inducing and Visualizing Clustering”
This chapter describes how to generate and use clustering to explore data. This tool is useful to detect groups of records that have similar characteristics.

Chapter 17, “Column Importance”
This chapter provides a complete description of the column importance tool. It also describes the relationship between column importance and the importance ranking in the other data mining tools.

Chapter 18, “Selection and Drill-Through”
This chapter describes the how to use multiple selection in the MineSet tools, as well as the concept of drill-through.

Chapter 19, “File Exchange Between MineSet and SAS”
This chapter describes the support for file exchanges between the MineSet and SAS formats.

Chapter 20, “MineSet Web Extensions”
This chapter describes the MineSet extensions that are provided to let you create or view visualizations and/or interact with MineSet over the web.

Appendix A, “Flat File Support for MineSet”
This appendix describes the .schema and the .data files that are required for MineSet to read flat files.

Appendix B, “Creating Data and Configuration Files for the Tree Visualizer”
This appendix explains the required formats of the Tree Visualizer data and configuration files.

Appendix C, “Creating Data, Configuration, Hierarchy, and GFX Files for the Map Visualizer”
This appendix explains the required formats of the Map Visualizer data, configuration, hierarchy, and .gfx files.

Appendix D, “Creating Data and Configuration Files for the Scatter Visualizer”
This appendix explains the required formats of the Scatter Visualizer data and configuration files.

Appendix E, “Creating Data and Configuration Files for the Splat Visualizer”
This appendix describes the format of the Splat Visualizer's data file.

Appendix F, “Creating Data and Configuration Files for the Rules Visualizer”
This appendix explains the required formats of the Rules Visualizer data and configuration files.

Appendix G, “Format of the Evidence Visualizer's Data File”
This appendix describes the format of the Evidence Visualizer's data file.

Appendix H, “Creating Data and Configuration Files for the Decision Table Visualizer”
This appendix describes the format of the Decision Table's data file.

Appendix I, “Command-Line Interface to MIndUtil: Analytical Data Mining Algorithms”
This appendix describes how the server side of the MineSet images handles classifiers, regressors, discretization, column importance, file conversions, and their options.

Appendix J, “Nulls in MineSet”
This appendix describes how MineSet supports nulls in the data access tools, the mining tools, and the visualization tools.

Appendix K, “Further Reading and Acknowledgments”
This appendix lists reference sources for further reading about concepts and their implementations used in the MineSet tools. It also lists acknowledgments for data sources used in the examples provided with these tools.

The hard copy of this documentation provides all screen shots and illustrations in black and white. The online version, however, provides these visuals in full, original color. Thus, if you are reading the hard copy version and find a particular graphic or screen shot difficult to see, go to the respective page of the online version for greater clarity.

The following type conventions and symbols are used in this guide: